{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ruled-synthesis",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(style=\"whitegrid\", palette=\"muted\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "attractive-circumstances",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1M.windows.bed               \u001b[35mracon.round3.vcf\u001b[m\u001b[m\r\n",
      "1M.windows.dv.cov.bed        regions.bed\r\n",
      "1M.windows.rc.cov.bed        run_bedtools.sh\r\n",
      "1M.windows.rm.cov.bed        \u001b[35msv_small_no_rDNA.vcf\u001b[m\u001b[m\r\n",
      "plots.ipynb                  \u001b[35mt2t-chm13.20200904.fasta.fai\u001b[m\u001b[m\r\n",
      "\u001b[35mracon.round3.merfin.vcf\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "brave-paste",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dv = pd.read_csv(\"1M.windows.dv.cov.bed\", sep=\"\\t\", names=[\"chrom\", \"start\", \"end\", \"cov\", \"x\", \"y\", \"z\"])\n",
    "df_dv[\"Polisher\"] = \"DeepVariant\"\n",
    "\n",
    "df_rc = pd.read_csv(\"1M.windows.rc.cov.bed\", sep=\"\\t\", names=[\"chrom\", \"start\", \"end\", \"cov\", \"x\", \"y\", \"z\"])\n",
    "df_rc[\"Polisher\"] = \"Racon\"\n",
    "\n",
    "df_rm = pd.read_csv(\"1M.windows.rm.cov.bed\", sep=\"\\t\", names=[\"chrom\", \"start\", \"end\", \"cov\", \"x\", \"y\", \"z\"])\n",
    "df_rm[\"Polisher\"] = \"Racon-Merfin\"\n",
    "\n",
    "df = pd.concat([df_dv, df_rc, df_rm])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "logical-modem",
   "metadata": {},
   "source": [
    "# Plotting the Distribution\n",
    "First, we will plot the distribution of the number of polishing edits in 1 Mbp windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "chronic-bruce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# color pallete: https://colorbrewer2.org/?type=qualitative&scheme=Dark2&n=3\n",
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "sns.histplot(data=df,\n",
    "             x=\"cov\",\n",
    "             hue=\"Polisher\",\n",
    "             bins=100,\n",
    "             ax=ax,\n",
    "             element=\"step\",\n",
    "             fill=False,\n",
    "             palette=[\"#1b9e77\", \"#d95f02\", \"#7570b3\"]\n",
    "            )\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"# Corrections in 1 Mbp Windows\")\n",
    "plt.axvline(60, color=\"red\")\n",
    "plt.savefig(\"polishing_edit_hist.pdf\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
